Published

2025-01-01

Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate

Modelación de diseños experimentales incluyendo datos longitudinales y una covariable funcional

DOI:

https://doi.org/10.15446/rce.v48n1.113398

Keywords:

Basis functions, Chlorophyll concentration, Functional data analysis, Functional principal components analysis, Random coefficient model, Spectral signature. (en)
Análisis de componentes principales funcionales, Análisis de datos funcionales, Base de funciones, Concentración de clorofila, Firma espectral, Modelo de coeficientes aleatorios. (es)

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The study of longitudinal measures of chlorophyll concentrations is key to reducing the risk of yield-limiting deficiencies or costly over fertilizing. Factors as irrigation and fertilization can influence the chlorophyll content. In this research we analyzed data from a experimental design of chlorophyll concentrations in chili pepper plants under the effect of two factors (fertilizer and irrigation, both with four levels) recorded weekly (for seven weeks). The spectral signature curves obtained for each plant was included in the model as a functional covariate. We propose an alternative for the analysis of data from experimental designs involving longitudinal data (LD) and a  functional covariate. Two smoothing approaches using basis functions and functional principal component reduce the problem to the application of a Linear Mixed Model (LMM) to LD in the presence of multiple scalar covariates. In both approaches, the results indicate that the inclusion of the functional covariate (spectral signature) contributes to explain the relationship between the chlorophyll concentration and the factors analyzed.

El estudio de mediciones longitudinales de concentraciones de clorofila es clave para reducir el riesgo de deficiencias que limiten el crecimiento o de una fertilización excesiva y costosa. Factores como la irrigación y la fertilización pueden influir en el contenido de clorofila. En esta investigación analizamos datos de un diseño experimental de concentraciones de clorofila en plantas de ají picante, bajo el efecto de dos factores (fertilizante e irrigación, ambos con cuatro niveles), registrados semanalmente (durante siete semanas). Las curvas de firma espectral obtenidas por cada planta se incluyeron en el modelo como una covariable funcional. Proponemos una alternativa para el análisis de datos de diseños experimentales que involucran datos longitudinales (DL) y una covariable funcional. Dos enfoques de suavización que utilizan funciones base y componentes principales funcionales reducen el problema a la aplicación de un modelo lineal mixto (MLM) a DL en presencia de múltiples covariables escalares. En ambas alternativas, los resultados indican que la inclusión de una covariables funcional (firma espectral) contribuye a explicar la relación entre la concentración de clorofila y los factores analizados.

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How to Cite

APA

Gómez-Escobar, G. A., Andrade-Bejarano, M. and Giraldo, R. (2025). Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate. Revista Colombiana de Estadística, 48(1), 177–193. https://doi.org/10.15446/rce.v48n1.113398

ACM

[1]
Gómez-Escobar, G.A., Andrade-Bejarano, M. and Giraldo, R. 2025. Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate. Revista Colombiana de Estadística. 48, 1 (Jan. 2025), 177–193. DOI:https://doi.org/10.15446/rce.v48n1.113398.

ACS

(1)
Gómez-Escobar, G. A.; Andrade-Bejarano, M.; Giraldo, R. Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate. Rev. colomb. estad. 2025, 48, 177-193.

ABNT

GÓMEZ-ESCOBAR, G. A.; ANDRADE-BEJARANO, M.; GIRALDO, R. Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate. Revista Colombiana de Estadística, [S. l.], v. 48, n. 1, p. 177–193, 2025. DOI: 10.15446/rce.v48n1.113398. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/113398. Acesso em: 19 feb. 2025.

Chicago

Gómez-Escobar, Gustavo Adolfo, Mercedes Andrade-Bejarano, and Ramón Giraldo. 2025. “Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate”. Revista Colombiana De Estadística 48 (1):177-93. https://doi.org/10.15446/rce.v48n1.113398.

Harvard

Gómez-Escobar, G. A., Andrade-Bejarano, M. and Giraldo, R. (2025) “Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate”, Revista Colombiana de Estadística, 48(1), pp. 177–193. doi: 10.15446/rce.v48n1.113398.

IEEE

[1]
G. A. Gómez-Escobar, M. Andrade-Bejarano, and R. Giraldo, “Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate”, Rev. colomb. estad., vol. 48, no. 1, pp. 177–193, Jan. 2025.

MLA

Gómez-Escobar, G. A., M. Andrade-Bejarano, and R. Giraldo. “Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate”. Revista Colombiana de Estadística, vol. 48, no. 1, Jan. 2025, pp. 177-93, doi:10.15446/rce.v48n1.113398.

Turabian

Gómez-Escobar, Gustavo Adolfo, Mercedes Andrade-Bejarano, and Ramón Giraldo. “Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate”. Revista Colombiana de Estadística 48, no. 1 (January 21, 2025): 177–193. Accessed February 19, 2025. https://revistas.unal.edu.co/index.php/estad/article/view/113398.

Vancouver

1.
Gómez-Escobar GA, Andrade-Bejarano M, Giraldo R. Modeling Experimental Designs Including Longitudinal Data and a Functional Covariate. Rev. colomb. estad. [Internet]. 2025 Jan. 21 [cited 2025 Feb. 19];48(1):177-93. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/113398

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